Single-step synthesis of titanium nitride-oxide composite and AI-driven aging forecast for lithium-sulfur batteries

In this study, the polysulfide shuttle effect, a major impediment to the efficiency of lithium-sulfur (Li-S) batteries, is addressed. A titanium nitride-oxide (TiO2-TiN) composite is synthesized via a single-step liquid-phase reaction at 60 degrees C only, significantly streamlining the production for large-scale applications. This composite, serving as a cathode material in Li-S batteries, demonstrates remarkable performance, with an initial capacity of 774 mA h g-1, and maintains 517 mA h g-1 after 500 cycles at a 0.5C rate with a decay rate of 0.066% per cycle. The integration of a Super P carbon-coated separator further enhances the battery performance, achieving an initial capacity of 926 mA h g-1 and maintaining 628 mA h g-1 after 500 cycles, with the lower decay rate of 0.064% per cycle. Moreover, the integration of Long Short-Term Memory (LSTM) networks into data analysis has facilitated the creation of a deep learning-based predictive model. This model is adept at accurately forecasting the aging effects of batteries up to 100 cycles in advance. This AI-driven approach represents a novel paradigm in battery research, offering the potential to expedite the battery testing process and streamline quality control procedures. Such advancements are pivotal in making the commercialization of Li-S batteries more feasible and efficient. This study presents a single-step synthesis of a TiO2-TiN composite for Li-S batteries, using AI for aging forecasts, streamlining processes and leading to safer, more sustainable production.